The results presented in Table REF  show consistency with respect to which system performs the best despite the variation in their rankings.
The Varifocal systems outperform BART and SQuAD by around 4 ROUGE-1 points, >4 METEOR points, and show lower error rates based on TER.
Interestingly, SQuAD slightly outperforms BART despite not being trained on the QABriefs dataset. However, SQuAD uses focal points to generate questions, which indicates their potential usefulness.
We notice a minor difference between Varifocal and Varifocal+Meta except for TER, where Varifocal (without metadata) performs slightly better.
To further assess the potential of focal points, we experimented with the BART question generator . We forced a BART model (wh-BART) to initiate the generated questions with the most common question words in the QABriefs dataset, typically wh-words such as What, Why, How, etc. (see Figure 4 in [1]}). While this resulted in scores comparable to those of BART and SQuAD, these were always lower than the scores achieved by Varifocal, further demonstrating that focal points provide guidance beyond the question type.
